Publications: Structural diversity

Knowledge complexity is an essential dimension of knowledge production in today’s economy. Foremost, this concerns the question to what extent this dimension decides about the economic growth of organizations, regions, and countries. The argument of complexity as a competitive advantage states that complex knowledge is a critical resource yielding higher economic outcomes than simpler activities. However, the empirical evidence for this claim is scarce and restricted to country’s economic complexity as one dimension of knowledge complexity. We contribute to this literature by investigating the relationship between economic growth and technological complexity, which represents an alternative dimension of knowledge complexity. Panel regressions for 166 European NUTS 2 regions covering the years 2000 to 2015 suggest that technological complexity is a positive predictor of economic growth at the regional level. A ten percent increase in regional complexity is associated with a corresponding GDP per capita growth by about 0.28 percent. The conducive role of knowledge complexity for economic growth is therefore not only evident for countries, but also for regions revealing a relationship, which is systematic at different spatial scales and for different types of knowledge complexity.

The paper introduces structural diversity as a new approach to quantify the complexity of technologies. By modeling technologies as combinatorial networks, a measure of technological complexity is derived that represents the diversity of (sub-)network topologies in these networks. It is further argued that this measure can be empirically approximated with the Network Diversity Score (NDS). The paper also presents an application of this approach to European patent data from 1980 to 2015. On this basis, the measure of structural diversity is shown to replicate a number of stylized facts commonly associated with technological complexity: Complexity increases over time and younger technologies are more complex than older technologies. Complex technologies are also associated to larger R&D efforts and require more collaborative R&D activities. Lastly, when controlling for technologies’ size, technologies scoring high on structural diversity are also shown to concentrate in space.

Measuring technological complexity – Current approaches and a new measure of structural complexity (pdf).

The paper reviews two prominent approaches for the measurement of technological complexity: the method of reflection and the assessment of technologies’ combinatorial difficulty. It discusses their central underlying assumptions and identifies potential problems related to these. A new measure of structural complexity is introduced as an alternative. The paper also puts forward four stylized facts of technological complexity that serve as benchmarks in an empirical evaluation of five complexity measures (increasing development over time, larger R&D efforts, more collaborative R&D, spatial concentration). The evaluation utilizes European patent data for the years 1980 to 2013 and finds the new measure of structural complexity to mirror the four stylized facts as good as or better than traditional measures.